GleanEnterprise
An enterprise AI platform that connects to company apps to power permission-aware search, an assistant, and agents over internal knowledge.
AI / LLM comparison
Glean vs LangGraph
Pricing, pros, cons, and ideal use cases — side by side.
LangGraphFreemium
A low-level orchestration framework for building stateful, multi-step agent workflows with explicit control over state, branching, and human-in-the-loop steps.
At a glance
| Glean | LangGraph | |
|---|---|---|
| Pricing | EnterpriseEnterprise pricing, quoted per organization and typically seat-based. | FreemiumOpen-source framework, free to use. Paid tiers apply to the surrounding LangSmith platform, not the framework itself. |
| Category | AI / LLM | AI / LLM |
| Ideal for | Large enterprises with knowledge spread across many toolsTeams deploying an internal AI assistantOrganizations that need permission-aware AI search | Engineering teams building multi-step agentsEnterprise platform teamsWorkflows needing human-in-the-loop control |
Pros & cons
Glean
Pros
- Permission-aware search across the whole app estate
- Mature connector library for enterprise SaaS
- Assistant and agent layer on a trusted knowledge graph
- Strong fit for large, complex organizations
Cons
- Enterprise pricing and a real procurement process
- Value depends on broad connector deployment
- Overkill for small teams or simple stacks
LangGraph
Pros
- Explicit control over agent state and branching
- First-class human-in-the-loop and checkpointing
- Strong fit for durable, long-running workflows
- Large ecosystem and active development
Cons
- Requires real engineering investment
- Lower-level than no-code agent builders
- You own deployment and observability
Which should you choose?
LangGraph is the lighter-weight option (Freemium), while Glean sits higher on the pricing ladder (Enterprise). Glean is built around large enterprises with knowledge spread across many tools; LangGraph leans more toward engineering teams building multi-step agents. Shortlist the one whose strengths line up with your biggest constraint.